ci-pipeline-generator
Generator สำหรับสร้าง CI/CD pipeline configs (GitHub Actions, GitLab CI) พร้อม testing, linting, building และ deployment
Découvrez les skills Bases de données, comparez leurs dépôts et consultez leurs instructions.
Generator สำหรับสร้าง CI/CD pipeline configs (GitHub Actions, GitLab CI) พร้อม testing, linting, building และ deployment
Continuous monitoring and diagnostics for ensuring high availability, performance, and stability of database systems.
Automated code quality enforcement through linting, formatting, and type checking to maintain consistent, error-free codebases.
See the main Schema Drift Detection skill for comprehensive coverage of schema drift detection and management.
Implementing human override capabilities for AI decisions with proper tracking, auditing, and feedback loops.
Policy สำหรับ enforce code quality ผ่าน linting, testing, และ type checking ใน CI pipeline
Advanced WebSocket patterns for real-time bidirectional communication in web applications.
Comprehensive runbooks and playbooks for on-call incident response
Defining clear ownership models and escalation procedures to ensure incidents are handled by the right people at the right time.
Observability standards for services: structured logging schema, metrics naming and label rules, trace context propagation, correlation IDs, sensitive-data handling, and cardinality controls
How to organize and index documentation for humans and AI: taxonomy, metadata, cross-linking, freshness reviews, and automated index generation
Comprehensive onboarding documentation to get new developers productive quickly with clear guides, checklists, and resources.
Comprehensive guide to database transactions, ACID properties, isolation levels, locking, and distributed transactions.
Rapid assessment and initial response processes for production incidents
Techniques and tools for ensuring the accuracy, completeness, and reliability of data across the pipeline.
Implementing human approval and review workflows for AI systems to ensure quality, safety, and compliance.
Architecture patterns and strategies for multi-cloud deployments to avoid vendor lock-in.
Comprehensive guide to identifying, visualizing, and managing dependencies in complex projects to reduce risks and enable coordination
Implementing distributed transactions across microservices using the Saga pattern with choreography and orchestration approaches.
Implementing and optimizing database connection pools for high-performance applications.
Environment standards for development, staging, and production: parity principles, data and access policies, configuration boundaries, and deployment controls
Operational patterns for multi-tenant systems: tenant-safe debugging and monitoring, per-tenant maintenance and migrations, fairness controls, and compliance-aware access
Comprehensive guide to using LLMs as judges for automated evaluation including prompt patterns, calibration, bias reduction, and multi-judge ensembles
Structured process for reviewing and validating architectural decisions before implementation to catch issues early.
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.